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Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So
by larodi 2y ago
Those Apple engineers stated in a very clear tone:
- every time a different result is produced.
- no reasoning capabilities were categorically determined.
So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are.
Otherwise simply forget this approach, and particularly when you need reproducible discreet results.
I don’t think it gets any better than that and nothing so far implicated it will (with this particular approach to AGI or whatever the wet dream is)
- marcellus23 2y ago> Those Apple engineers Which Apple engineers? Yours is the only reference to the company in this comment section or in the article.
- Agingcoder 2y agoSee arxiv paper just above
- larodi 2y agoSorry I thought this was already discussed in HN in a major topic, and was hard for me to copy page the link on mobile. Please take excuse.
- verteu 2y ago(for reference: https://arxiv.org/pdf/2410.05229 https://arxiv.org/pdf/2410.05229 )
- rco8786 2y agoThere’s another option here though. Human supervised tasks. There’s a whole classification of tasks where a human can look at a body of work and determine whether it’s correct or not in far less time than it would take for them to produce the work directly. As a random example, having LLMs write unit tests.
- jacobr1 2y agoWhich is a good example, because accuracy can be improved significantly with even minor human guidance in task like unit tests. Human augmentation is extremely valuable.
- osigurdson 2y agoI wonder if there is a moral hazard here? Apple doesn't really have much in terms of AI, so maybe more likely to have an unfavorable view.
- larodi 2y agoNo sadly they just voicing the opinion already voiced by (many) other scientists. My masters was text-to-sql and I can tell you hundreds of papers conclude that seq2seq and the transformer dérivâtes suck at logic even when you approach logic the symbolic way. We’d love to figure production rules of any sort emerge with scale of the transformer, but I’m get to read such paper.
- fennecfoxy 2y agoIt's also true that Apple hasn't really created any of these technologies themselves; afaik they're using a mostly standard LLM architecture (not invented by Apple) combined with task specific LORAs (not invented by Apple). Has Apple actually created any genuinely new technologies or innovations for Apple Intelligence?